Machine Learning for Modeling Wireless Radio Metrics with Crowdsourced Data and Local Environment Features
This work supports precise network planning and quality of service optimization for wireless networks in complex Canadian urban environments, representing an incremental improvement with domain-specific application.
This paper tackled the problem of modeling wireless radio metrics like RSRP, RSRQ, and RSSI in 4G environments using crowdsourced data and local environmental features, achieving RMSE performance ranging from 9.76 to 11.69 dB for RSRP, 2.90 to 3.23 dB for RSRQ, and 9.50 to 10.36 dB for RSSI on over 300,000 data points in Canadian urban areas.
This paper presents a suite of machine learning models, CRC-ML-Radio Metrics, designed for modeling RSRP, RSRQ, and RSSI wireless radio metrics in 4G environments. These models utilize crowdsourced data with local environmental features to enhance prediction accuracy across both indoor at elevation and outdoor urban settings. They achieve RMSE performance of 9.76 to 11.69 dB for RSRP, 2.90 to 3.23 dB for RSRQ, and 9.50 to 10.36 dB for RSSI, evaluated on over 300,000 data points in the Toronto, Montreal, and Vancouver areas. These results demonstrate the robustness and adaptability of the models, supporting precise network planning and quality of service optimization in complex Canadian urban environments.